Spam image email filtering using K-NN and SVM

The developing utilization of web has advanced a simple and quick method for e-correspondence. The outstanding case for this is e-mail. Presently days sending and accepting email as a method for correspondence is prominently utilized. Be that as it may, at that point there stand up an issue in particular, Spam mails. Spam sends are the messages send by some obscure sender just to hamper the improvement of Internet e.g. Advertisement and many more.  Spammers introduced the new technique of embedding the spam mails in the attached image in the mail. In this paper, we proposed a method based on combination of SVM and KNN. SVM tend to set aside a long opportunity to prepare with an expansive information set. On the off chance that "excess" examples are recognized and erased in pre-handling, the preparation time could be diminished fundamentally. We propose a k-nearest neighbor (k-NN) based example determination strategy. The strategy tries to select the examples that are close to the choice limit and that are effectively named. The fundamental thought is to discover close neighbors to a question test and prepare a nearby SVM that jelly the separation work on the gathering of neighbors. Our experimental studies based on a public available dataset (Dredze) show that results are improved to approximately 98%

Feature normalization for part-based image classification

Part-based Bag-of-Features (BoF) models such as Spatial Pyramid Matching (SPM) play an important role in image classification. Before sending the feature vectors into classi-fiers for training and testing, it is required to normalize them in order to approximately equalize ranges of the attributes and make them have comparable effects in distance computation. Although some works have been focused on general feature normalization, we do not see any discussion on specialized normalization algorithms for part-based BoF models. In this paper, we fill in the blank with extensive experi-ments and discussions. Based on solid normalization param-eters (power and coefficient), we further study two straight-forward part-based properties, i.e., the independent assump-tion and the hierarchical-contribution assumption, to scale the feature super-vectors separately. Finally, we test our algorith-m on challenging image sets, i.e., Caltech101 and CUB-200-2011, for general and fine-grained classification, and show its efficiency, scalability and adaptability in both scenarios